Adverse drug events remain a leading cause of morbidity and mortality in the United States and around the world. In addition, nearly 30% of investigated drugs fail clinical trials due to unexpected adverse events. Large collections of adverse drug event reports are maintained by the Food and Drug Administration and other organizations.
Currently, hypotheses about drug side effects are generated through quantitative signal detection. These methods compare the expected reporting frequencies between drugs and side effects to the actual frequencies. But uncharacterized biases in spontaneous reporting systems, such as prescription bias, patient demographic biases, concomitant drug use, and co-morbidities, significantly limit the effectiveness of these algorithms.